Courses

Over 20+ years I have taught graduate and undergraduate courses across four institutions on two continents — spanning low-level programming (Assembly, C/C++, Pascal, Java), AI, data mining, database systems, spatial and scientific databases, and data science ethics. Descriptions and catalog links are provided for current GSU offerings.

Georgia State University (2013–present)

CSC 6710 — Database Systems

Graduate, 4 credit hours | Prerequisites: CSC 2720

Introduction to the fundamental concepts and principles underlying the relational model of data. Topics include formal query languages, SQL, query optimization, relational database design theory, physical database design, integrity, security, and concurrency control.

CSC 6780 — Data Science

Graduate, 4 credit hours | Prerequisites: CSC 2720

Introduction to the fundamental concepts of predictive data science for tabular data with qualitative and quantitative scales. Topics include data exploration, preprocessing and visualization, analytics base table (ABT) generation, and basic supervised learning algorithms — including information-based, similarity-based, and error-based learning — with comparative evaluation of these approaches.

CSC 6740 — Data Mining

Graduate, 4 credit hours | Prerequisites: CSC 2720

Introduction to basic data mining techniques — including association rule mining, cluster analysis, and classification methods — and their applications in web data mining, biomedical data mining, and security. The course follows established data science methodologies such as KDD (Knowledge Discovery in Databases) and SEMMA (Sample, Explore, Modify, Model, Assess), providing students with a structured framework for end-to-end data mining projects on real-world datasets.

CSC 8713 — Spatial and Scientific Databases

Graduate, 4 credit hours | Prerequisites: CSC 6710 (B or better)

Advanced concepts in spatial databases, high-dimensional data indexing (with applications in content-based image retrieval through kNN querying), data warehouses, and emerging spatiotemporal database systems. Students gain hands-on experience through an individual graduate research project on a chosen aspect of database systems.

CSC 8740 — Advanced Data Mining

Graduate, 4 credit hours | Prerequisites: CSC 6710 and CSC 6740 (B or better)

Advanced data mining topics including sequence data analysis, time-series classification and forecasting (dynamic time warping, kNN classifiers), high-dimensional data analysis with applications to indexing, and spatiotemporal pattern discovery. Provides students with sufficient foundation to conduct supervised research on mining unconventional data (image, time-series, spatiotemporal) from large real-world repositories. Primary course supporting the BDML concentration.

CSC 8902 — Ethics for Data Science

Graduate, 1 credit hour

General introduction to ethics in data science through readings and case studies. Covers the context and skills for ethically collecting, storing, sharing, and analyzing data — including awareness of preserving privacy, avoiding bias, and mitigating malicious attacks. Developed as part of the BDML curriculum initiative.

Montana State University (2004–2013)

Earlier Teaching (1999–2006)

University of Szczecin, Poland | Al Akhawayn University, Morocco

Taught undergraduate and graduate courses in expert systems, databases, fuzzy logic, neural networks, assembly language, Java, C/C++, Pascal, and information systems as part of early instructor and visiting faculty positions.

For current course offerings and schedules, see the GSU Computer Science course catalog.